Dr. Michael A. Bukatin

Mishka’s theoretical interests concentrate in applications of
continuous
mathematics to computing — domains for denotational semantics give
the
prime example of such applications. Another important area of such
applications is realistic neural networks.

His practical interests are in programming language design and
implementation, in methods and tools for software engineering, and in
audio-visual computer-based cognitive tools.

His AI-related interests include automation of programming,
optimization over
spaces of programs, machine learning over spaces of programs (“symbolic
regression”), algebra and analysis over spaces of programs,
neuroscience, spiking neural nets, mathematics of heterogeneous spaces,
philosophy of general AI (“AGI”), including research related to the
“hard problem of consciousness”, problems related to uploading,
and problems
related to the technological singularity and friendly AI.

There is a wide range of approaches to the issue of AI friendliness and
not much consensus between researchers in this field. This is quite
appropriate given how unpredictable is the situation in the field of
software, and how little if anything can we understand about the
post-singularity dynamics.

On one pole there are people who hope for and work towards a formal
framework for developing friendly AI and formal guarantees of
friendliness. This seems to be a highly attractive approach, despite
rather widespread doubts regarding its feasibility, but there is a
strong chance that it will not be competitive enough compared to the
other, more straight routes to general AI.

Motivated by rather slow progress in formal methods for AI friendliness,
alternative approaches look at various possible flavors of AI and
compare their chances of being friendly. There is a wide diversity of
opinion here as well.

Then there is another pole — the opposite one to the pole of
formal
guarantees — which is somewhat underrepresented. It is an approach
which
might seem strange at the first glance. Can a text (whether in a natural
language or in some formal language), or another chunk of information,
which is not a part of the “winning AGI system” itself, but merely a
small part of its input (e.g. downloaded by the AGI in question from the
Web) make a difference where friendliness is concerned?

Mishka wants to add a few words about this underrepresented pole, with
the
hope to encourage people to think more about it. First of all, this
approach relies on an assumption that the AGI in question will have
reasonable capabilities to understand, interpret, and use human-created
texts or other artifacts. If that is not the case, our chances for
friendliness will probably be quite low. However, the ability to
meaningfully handle human-created texts and other artifacts seems to
provide a significant initial competitive advantage for AGIs. This
inspires hope that such an ability will be present.

We note that from an “ordinary human point of view” this approach makes
more sense than enforcement of formal constraints. It is natural to try
to talk to your child (who in this case is supposed to be much smarter
than you), and this appears somehow “more decent” than manipulating the
situation in order to constrain the behavior of your (exceedingly smart)
child.

We do see an occasional “letter to a future general AI” about ethical
issues, and any such letter is an attempt at this
approach.

This is also not a bad approach from a computer science viewpoint: data
and programs are the same thing, hence any input to a program should be
viewed as a chunk of code in the language defined by the program in
question. But Mishka has not seen any attempts to analyze the situation
for
different possible architectures of AI, or to take into account the vast
sea of inputs of various kinds (including the body of all texts on
friendly AI!), or anything like that. It is such a “metalevel analysis”
of this approach that seems to be completely missing.

Mishka earned his M.S. in Applied Mathematics at
the Moscow Institute of Railroad Engineers in 1986.
He earned his Ph.D. in Computer Science at
Brandeis University in 2002 with the dissertation
Mathematics of Domains.